multimodalart HF staff commited on
Commit
3e27b3e
1 Parent(s): 82cf36d

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +4 -2
app.py CHANGED
@@ -3,7 +3,7 @@ from PIL import Image
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  import requests
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  import subprocess
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  from transformers import Blip2Processor, Blip2ForConditionalGeneration
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- from huggingface_hub import snapshot_download
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  import torch
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  import uuid
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  import os
@@ -114,6 +114,7 @@ def change_defaults(option, images):
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  return max_train_steps, repeats, lr_scheduler, rank, with_prior_preservation, class_prompt, random_files
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  def create_dataset(*inputs):
 
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  images = inputs[0]
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  destination_folder = str(uuid.uuid4())
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  print(destination_folder)
@@ -186,6 +187,7 @@ def start_training(
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  token,
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  progress = gr.Progress(track_tqdm=True)
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  ):
 
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  slugged_lora_name = slugify(lora_name)
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  spacerunner_folder = str(uuid.uuid4())
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  commands = [
@@ -702,7 +704,7 @@ To improve the quality of your outputs, you can add a custom caption for each im
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  local_rank = gr.Number(label="local_rank", value=-1)
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  token = gr.Textbox(label="Your Hugging Face write token", info="A Hugging Face write token you can obtain on the [settings page](#).")
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  start = gr.Button("Start training", visible=False)
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- progress_area = gr.HTML()
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  output_components.insert(1, advanced)
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  output_components.insert(1, start)
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  use_snr_gamma.change(
 
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  import requests
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  import subprocess
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  from transformers import Blip2Processor, Blip2ForConditionalGeneration
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+ from huggingface_hub import snapshot_download, HfApi
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  import torch
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  import uuid
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  import os
 
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  return max_train_steps, repeats, lr_scheduler, rank, with_prior_preservation, class_prompt, random_files
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  def create_dataset(*inputs):
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+ print("Creating dataset")
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  images = inputs[0]
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  destination_folder = str(uuid.uuid4())
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  print(destination_folder)
 
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  token,
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  progress = gr.Progress(track_tqdm=True)
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  ):
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+ print("Started training")
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  slugged_lora_name = slugify(lora_name)
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  spacerunner_folder = str(uuid.uuid4())
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  commands = [
 
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  local_rank = gr.Number(label="local_rank", value=-1)
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  token = gr.Textbox(label="Your Hugging Face write token", info="A Hugging Face write token you can obtain on the [settings page](#).")
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  start = gr.Button("Start training", visible=False)
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+ progress_area = gr.HTML("...")
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  output_components.insert(1, advanced)
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  output_components.insert(1, start)
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  use_snr_gamma.change(